Statistical analysis of several factors in predicting student performance

Yuyao Jiang

doi:10.1117/12.2628062

22 April 2022 Statistical analysis of several factors in predicting student performance

Yuyao Jiang

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121630D (2022) https://doi.org/10.1117/12.2628062
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

Education has always been a key factor in a country's development. Improving student performance is a common goal of students, parents and teachers. Studying the factors that influence student performance can help students focus on their weak points to improve their final grades more effectively. In this paper, random forest algorithm is used to extract the four most important independent variables, which are second-period grade (G2), first-period grade (G1), number of school absences (absences) and number of past class failures (failures) from a dataset on student performance. Then a multiple linear regression model is then established to study the relationship between the dependent variable final grade (G3) and them. After the evaluation of the model fitting accuracy and residual test, a linear model (y = -1.76483 + 0.97847 X₁ + 0.14374 X₂ + 0.03759 X₃ - 0.25720 X₄) is built. It simplifies the model and can predict student performance with great accuracy

Citation Download Citation

Yuyao Jiang "Statistical analysis of several factors in predicting student performance", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121630D (22 April 2022); https://doi.org/10.1117/12.2628062

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